Improving games AI performance using grouped hierarchical level of detail

Computer games are increasingly making use of large environments; however, these are often only sparsely populated with autonomous agents. This is, in part, due to the computational cost of implementing behaviour functions for large numbers of agents. In this paper we present an optimisation based on level of detail which reduces the overhead of modelling group behaviours, and facilitates the population of an expansive game world. We consider an environment which is inhabited by many distinct groups of agents. Each group itself comprises individual agents, which are organised using a hierarchical tree structure. Expanding and collapsing nodes within each tree allows the efficient dynamic abstraction of individuals, depending on their proximity to the player. Each branching level represents a different level of detail, and the system is designed to trade off computational performance against behavioural fidelity in a way which is both efficient and seamless to the player. We have developed an implementation of this technique, and used it to evaluate the associated performance benefits. Our experiments indicate a significant potential reduction in processing time, with the update for the entire AI system taking less than 1% of the time required for the same number of agents without optimisation

A grid-based infrastructure for distributed retrieval

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Requirements Modeling for Multi-Agent Systems

Different approaches for building modern software systems in complex and open environments have been proposed in the last few years. Some efforts try to take advantage of the agent-oriented paradigm to model/engineer complex information systems in terms of independent agents. These agents may collaborate in a computational organization (Multi-Agent Systems, MAS) by playing some specific roles having to interact with others in order to reach a global or individual goal. In addition, due to the complex nature of this type of systems, dealing with the classical functional and structural perspectives of software systems are not enough. The organizational perspective, that describes the context where these agents need to collaborate, and the social behavior perspective, that describes the different "intelligent" manners in which these agents can collaborate, need to be identified and properly specified. Several methodologies have been proposed to drive the development of MAS (e.g., Ingenias, Gaia, Tropos) although most of them mainly focus on the design and implementation phases and do not provide adequate mechanisms for capturing, defining, and specifying software requirements. Poor requirements engineering is recognized as the root of most errors in current software development projects, and as a means for improving the quality of current practices in the development of MAS, the main objective of this work is to propose a requirements modeling process to deal with software requirements covering the functional, structural, organizational, and social behavior perspectives of MAS. The requirements modeling proposed is developed within the model-driven engineering context defining the corresponding metamodel and its graphical syntax. In addition, a MAS requirements modeling process is specified using the Object Management Group's (OMG) Software Process Engineering Metamodel (SPEM). Finally, in order to illustrate the feasibility of our approach, we specified the software requirements of a strategic board game (the Diplomacy game).Rodríguez Viruel, ML. (2011). Requirements Modeling for Multi-Agent Systems. http://hdl.handle.net/10251/11416Archivo delegad

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

The era of big data provides many opportunities for conducting impactful research from both data-driven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the data-driven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data

Dealing with Complexity in Agent-Oriented Software Engineering: The Importance of Interactions

Comisión Interministerial de Ciencia y Tecnología (CICYT) SETI (TIN2009-07366)Junta de Andalucía P07-TIC-2533 (Isabel)Junta de Andalucía TIC-590

2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k ($2^{18}$) processors. We present error analysis and scientific application results from a series of more than ten 69 billion ($4096^3$) particle cosmological simulations, accounting for $4 \times 10^{20}$ floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.Comment: 12 pages, 8 figures, 77 references; To appear in Proceedings of SC '1